Optimization of sentiment analysis using machine learning classifiers

Singh, Jaspreet; Singh, Gurvinder; Singh, Rajinder

doi:10.1186/s13673-017-0116-3

Cited by 143 publications

(64 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There have been lots of works for automatic news article classification [14]. • Opinion mining: It is very important to analyze the information on opinions, sentiment, and subjectivity in documents with a specific topic [15]. Analysis results can be applied to various areas such as website evaluation, the review of online news articles, opinion in blog or SNS, etc.…”

Section: Related Workmentioning

confidence: 99%

Research paper classification systems based on TF-IDF and LDA schemes

Kim

Gil

2019

Hum. Cent. Comput. Inf. Sci.

229

View full text Add to dashboard Cite

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

show abstract

Section: Related Workmentioning

confidence: 99%

Research paper classification systems based on TF-IDF and LDA schemes

Kim

Gil

2019

Hum. Cent. Comput. Inf. Sci.

229

View full text Add to dashboard Cite

show abstract

“…To optimize the SA task, Singh [19] employed four ML classifiers, i.e., Naïve Bayes, J48, BFTree, and OneR. NLTK and bs4 libraries were used for preprocessing of raw text.…”

Section: Related Workmentioning

confidence: 99%

Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews

2019

View full text Add to dashboard Cite

The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods.

show abstract

“…Soft computing techniques based SNA provides several new social data analysis solutions for social networking services, such as folksonomy mining [75], tag recommendation, social marketing [76], social recommendation and sentiment analysis [77]. Jaschke et al [78] proposed an algorithm for mining iceberg tri-lattices for mining the frequent tri-concepts.…”

Section: Social Data Analysismentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

show abstract

Optimization of sentiment analysis using machine learning classifiers

Cited by 143 publications

References 25 publications

Research paper classification systems based on TF-IDF and LDA schemes

Research paper classification systems based on TF-IDF and LDA schemes

Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews

When social computing meets soft computing: opportunities and insights

Contact Info

Product

Resources

About